{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"A very simple MNIST classifier.\n",
    "See extensive documentation at\n",
    "https://www.tensorflow.org/get_started/mnist/beginners\n",
    "\"\"\"\n",
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import argparse\n",
    "import sys\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "FLAGS = None\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "未用tinymind，用的自己的机器\n",
    "\n",
    "\n",
    "我们在这里调用系统提供的Mnist数据函数为我们读入数据，如果没有下载的话则进行下载。\n",
    "\n",
    "<font color=#ff0000>**这里将data_dir改为适合你的运行环境的目录**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting /tmp/tensorflow/mnist/input_data\\train-images-idx3-ubyte.gz\n",
      "Extracting /tmp/tensorflow/mnist/input_data\\train-labels-idx1-ubyte.gz\n",
      "Extracting /tmp/tensorflow/mnist/input_data\\t10k-images-idx3-ubyte.gz\n",
      "Extracting /tmp/tensorflow/mnist/input_data\\t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "# Import data\n",
    "data_dir = '/tmp/tensorflow/mnist/input_data'\n",
    "mnist = input_data.read_data_sets(data_dir, one_hot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "准备好placeholder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the model\n",
    "x = tf.placeholder(tf.float32, [None, 784])\n",
    "y_ = tf.placeholder(tf.float32, [None, 10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "初始化参数/权重\n",
    "此处我们添加了隐藏层\n",
    "（1）隐藏层初始化函数使用tf.truncated_normal()（截短的随机数）类型\n",
    "（2）中间层的激活函数，使用tanh函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "#创建一个简单的神经网络\n",
    "W1 = tf.Variable(tf.truncated_normal([784,600],stddev=0.1))\n",
    "b1 = tf.Variable(tf.zeros([600])+0.1)\n",
    "L1 = tf.nn.tanh(tf.matmul(x,W1)+b1) \n",
    " \n",
    "W2 = tf.Variable(tf.truncated_normal([600,450],stddev=0.1))\n",
    "b2 = tf.Variable(tf.zeros([450])+0.1)\n",
    "L2 = tf.nn.tanh(tf.matmul(L1,W2)+b2) \n",
    " \n",
    "W3 = tf.Variable(tf.truncated_normal([450,300],stddev=0.1))\n",
    "b3 = tf.Variable(tf.zeros([300])+0.1)\n",
    "L3 = tf.nn.tanh(tf.matmul(L2,W3)+b3)\n",
    "\n",
    "W4 = tf.Variable(tf.truncated_normal([300,150],stddev=0.1))\n",
    "b4 = tf.Variable(tf.zeros([150])+0.1)\n",
    "L4 = tf.nn.tanh(tf.matmul(L3,W4)+b4)\n",
    "\n",
    "W5 = tf.Variable(tf.truncated_normal([150,50],stddev=0.1))\n",
    "b5 = tf.Variable(tf.zeros([50])+0.1)\n",
    "L5 = tf.nn.tanh(tf.matmul(L4,W5)+b5)\n",
    " \n",
    "W6 = tf.Variable(tf.truncated_normal([50,10],stddev=0.1))\n",
    "b6 = tf.Variable(tf.zeros([10])+0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = tf.nn.softmax(tf.matmul(L5,W6)+b6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来我们计算交叉熵，注意这里不要使用注释中的手动计算方式，而是使用系统函数。\n",
    "另一个注意点就是，softmax_cross_entropy_with_logits的logits参数是**未经激活的wx+b**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The raw formulation of cross-entropy,\n",
    "#\n",
    "#   tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),\n",
    "#                                 reduction_indices=[1]))\n",
    "#\n",
    "# can be numerically unstable.\n",
    "#\n",
    "# So here we use tf.nn.softmax_cross_entropy_with_logits on the raw\n",
    "# outputs of 'y', and then average across the batch.\n",
    "cross_entropy = tf.reduce_mean(\n",
    "    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "生成一个训练step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_step = tf.train.GradientDescentOptimizer(0.7).minimize(cross_entropy)\n",
    "sess = tf.Session()\n",
    "init_op = tf.global_variables_initializer()\n",
    "sess.run(init_op)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这里我们仍然调用系统提供的读取数据，为我们取得一个batch。\n",
    "然后我们运行20k个step(5 epochs)，对权重进行优化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train\n",
    "for _ in range(20000):\n",
    "  batch_xs, batch_ys = mnist.train.next_batch(100)\n",
    "  sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "验证我们模型在测试数据上的准确率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9827\n"
     ]
    }
   ],
   "source": [
    "  # Test trained model\n",
    "correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "print(sess.run(accuracy, feed_dict={x: mnist.test.images,\n",
    "                                      y_: mnist.test.labels}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "毫无疑问，这个模型是一个非常简陋，性能也不理想的模型。目前只能达到92%左右的准确率。\n",
    "接下来，希望大家利用现有的知识，将这个模型优化至98%以上的准确率。\n",
    "Hint：\n",
    "- 多隐层\n",
    "- 激活函数\n",
    "- 正则化\n",
    "- 初始化\n",
    "- 摸索一下各个超参数\n",
    "  - 隐层神经元数量\n",
    "  - 学习率\n",
    "  - 正则化惩罚因子\n",
    "  - 最好每隔几个step就对loss、accuracy等等进行一次输出，这样才能有根据地进行调整"
   ]
  }
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